title: A QbD case study: Bayesian prediction of lyophilization cycle parameters.
abstract: As stipulated by ICH Q8 R2 (1), prediction of critical process parameters based on process modeling is a part of enhanced, quality by design approach to product development. In this work, we discuss a Bayesian model for the prediction of primary drying phase duration. The model is based on the premise that resistance to dry layer mass transfer is product specific, and is a function of nucleation temperature. The predicted duration of primary drying was experimentally verified on the lab scale lyophilizer. It is suggested that the model be used during scale-up activities in order to minimize trial and error and reduce costs associated with expensive large scale experiments. The proposed approach extends the work of Searles et al. (2) by adding a Bayesian treatment to primary drying modeling.
authors.affiliation: Purdue University, Discovery Park, West Lafayette, Indiana, USA. lmockus@purdue.edu
authors.forename: Linas
authors.initials: L
authors.lastname: Mockus
authors.forename: David
authors.initials: D
authors.lastname: LeBlond
authors.forename: Prabir K
authors.initials: PK
authors.lastname: Basu
authors.forename: Rakhi B
authors.initials: RB
authors.lastname: Shah
authors.forename: Mansoor A
authors.initials: MA
authors.lastname: Khan
mesh_terms: D001499:Bayes Theorem; D004339:Drug Compounding / Q000191:economics / Q000379:methods*; D005612:Freeze Drying; D006801:Humans; D015233:Models, Statistical; D011786:Quality Control; D012984:Software; D013678:Technology, Pharmaceutical; D013696:Temperature